The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
An Efficient Algorithm for Haplotype Inference on Pedigrees with Recombinations and Mutations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Computational methods for gene allele prediction have been proposed to substitute dedicated and expensive assays with cheaper in-silico analyses that operate on routinely collected data, such as SNP genotypes. Most of these methods are tailored to the needs and characteristics of human genetic studies where they achieve good prediction accuracy. However, genomic analyses are becoming increasingly important in livestock species too. For livestock species generally the underlying---usually quite large and complex---pedigree is known and available; this information is not fully exploited by current allele prediction methods. In this paper, we propose a new gene allele prediction method based on a simple, but robust, combinatorial formulation for the problem of discovering haplotype-allele associations. The inherent uncertainty of the haplotype inference process is reduced by taking into account the inheritance of gene alleles across the population pedigree while genotypes are phased. The accuracy of the method has been extensively evaluated on a representative real-world livestock dataset under several scenarios and choices of parameters. The median error rate ranged from 0.0537 to 0.0896, with an average of 0.0678; this is 21% better than another state-of-the-art prediction algorithm that does not use the pedigree information. The experimental results support the validity of the proposed approach and, in particular, of the use of pedigree information in gene allele predictions.